# Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices

arXiv: https://arxiv.org/abs/2106.01739 · HTML: https://mlsanity.com/paper/2106.01739/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Improving Neural Network Quantization without Retraining using Outlier Channel Splitting](https://mlsanity.com/paper/1901.09504/) (2019)
- [Ablation Studies in Artificial Neural Networks](https://mlsanity.com/paper/1901.08644/) (2019)
- [Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey](https://mlsanity.com/paper/1811.01238/) (2018)
- [CPT: Efficient Deep Neural Network Training via Cyclic Precision](https://mlsanity.com/paper/2101.09868/) (2021)
- [Bit Efficient Quantization for Deep Neural Networks](https://mlsanity.com/paper/1910.04877/) (2019)
- [A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints](https://mlsanity.com/paper/2011.14858/) (2020)
- [Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint](https://mlsanity.com/paper/2011.13741/) (2020)
- [Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network](https://mlsanity.com/paper/1702.00509/) (2017)
- [Recent Advances in Selective Image Encryption and its Indispensability due to COVID-19](https://mlsanity.com/paper/2011.13740/) (2020)

## Built on by (most cited first, 2 of 2)

- [The Need and Status of Sea Turtle Conservation and Survey of Associated Computer Vision Advances](https://mlsanity.com/paper/2107.14061/) (2021)
- [Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks](https://mlsanity.com/paper/2107.14070/) (2021)

## Abstract (arXiv)

Diabetic Retinopathy (DR) is a severe complication that may lead to retinal vascular damage and is one of the leading causes of vision impairment and blindness. DR broadly is classified into two stages - non-proliferative (NPDR), where there are almost no symptoms, except a few microaneurysms, and proliferative (PDR) involving a huge number of microaneurysms and hemorrhages, soft and hard exudates, neo-vascularization, macular ischemia or a combination of these, making it easier to detect. More specifically, DR is usually classified into five levels, labeled 0-4, from 0 indicating no DR to 4 which is most severe. This paper firstly presents a discussion on the risk factors of the disease, then surveys the recent literature on the topic followed by examining certain techniques which were found to be highly effective in improving the prognosis accuracy. Finally, a convolutional neural network model is proposed to detect all the stages of DR on a low-memory edge microcontroller. The model has a size of just 5.9 MB, accuracy and F1 score both of 94% and an inference speed of about 20 frames per second.